Computer Science > Computer Vision and Pattern Recognition
arXiv:2310.09236 (cs)
[Submitted on 13 Oct 2023]
Title:Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data
View a PDF of the paper titled Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data, by Pauline Mouches and 5 other authors
View PDFAbstract:Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain regions triggering seizures. Spike detection is often performed manually. However, it is a burdensome and error prone task due to the complexity of MEG data. To address this problem, we propose a 1D temporal convolutional neural network (Time CNN) coupled with a graph convolutional network (GCN) to classify short time frames of MEG recording as containing a spike or not. Compared to other recent approaches, our models have fewer parameters to train and we propose to use a GCN to account for MEG sensors spatial relationships. Our models produce clinically relevant results and outperform deep learning-based state-of-the-art methods reaching a classification f1-score of 76.7% on a balanced dataset and of 25.5% on a realistic, highly imbalanced dataset, for the spike class.
| Comments: | This work has been submitted to IEEE ISBI 2024 for possible publication |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2310.09236 [cs.CV] |
| (orarXiv:2310.09236v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2310.09236 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data, by Pauline Mouches and 5 other authors
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